4.7 Article

Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm

Publisher

ELSEVIER
DOI: 10.1016/j.future.2016.02.018

Keywords

Mobile spam filtering; Feature analysis; Hybrid machine learning; Information fusion; Immune system; Dendritic cell algorithm

Funding

  1. King Abdulaziz City for Science and Technology (KACST) through the Science AMP
  2. Technology Unit at King Fand University of Petroleum AMP
  3. Minerals (KFUPM), National Science, Technology and Innovation Plan [11-INF1658-04]

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With the continual growth of mobile devices, they become a universal portable platform for effective business and personal communication. They enable a plethora of textual communication modes including electronic mails, instant messaging, and short messaging services. A downside of such great technology is the alarming rate of spam messages that are not only annoying to end-users but raises security concerns as well. This paper presents an intelligent framework for filtering multimodal textual communication including emails and short messages. We explore a novel methodology for information fusion inspired by the human immune system and hybrid approaches of machines learning. We study a number of methods to extract and select more relevant features to reduce the complexity of the proposed model to suite mobile applications while preserving good performance. The proposed framework is intensively evaluated on a number of benchmark datasets with remarkable results achieved. (C) 2016 Elsevier B.V. All rights reserved.

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